Abstract: As an important branch in the field of computer vision,object tracking has been widely used in many fields such as intelligent video surveillance,human-computer interaction and autonomous driving.Although object tracking has achieved good development in recent years,tracking in complex environment is still a challenge.Due to problems such as occlusion,object deformation and illumination change,tracking performance will be inaccurate and unstable.In this paper,an effective object tracking method AFTM,is proposed with attention features.Firstly,this paper constructs an adaptively generated attention weight factor group,which implements an efficient adaptive fusion strategy for response map to improve the accuracy of object positioning and bounding box scale calculation in the process of classification and regression.Secondly,aiming at the class imbalance in the data set,the proposed method uses the dynamically scaled cross entropy loss as the loss function of the object positioning network,which can modify the optimization direction of the model and make the tracking performance more stable and reliable.Finally,this paper designs a corresponding learning rate adjustment strategy to stochastically average the weight of a number of models,which can enhance the generalization ability of the model.Experimental results on public data sets show that the proposed method has higher accuracy and more stable tracking performance in complex tracking environment.

Key words: Deep learning, Object tracking, Siamese network, Anchor-free, Attention mechanism